Efficient transfer entropy analysis of non-stationary neural time series

Information theory allows us to investigate information processing in neural systems in terms of information transfer, storage and modification. Especially the measure of information transfer, transfer entropy, has seen a dramatic surge of interest in neuroscience. Estimating transfer entropy from two processes requires the observation of multiple realizations of these processes to estimate associated probability density functions. To obtain these observations, available estimators assume stationarity of processes to allow pooling of observations over time. This assumption however, is a major obstacle to the application of these estimators in neuroscience as observed processes are often non-stationary. As a solution, Gomez-Herrero and colleagues theoretically showed that the stationarity assumption may be avoided by estimating transfer entropy from an ensemble of realizations. Such an ensemble is often readily available in neuroscience experiments in the form of experimental trials. Thus, in this work we combine the ensemble method with a recently proposed transfer entropy estimator to make transfer entropy estimation applicable to non-stationary time series. We present an efficient implementation of the approach that deals with the increased computational demand of the ensemble method's practical application. In particular, we use a massively parallel implementation for a graphics processing unit to handle the computationally most heavy aspects of the ensemble method. We test the performance and robustness of our implementation on data from simulated stochastic processes and demonstrate the method's applicability to magnetoencephalographic data. While we mainly evaluate the proposed method for neuroscientific data, we expect it to be applicable in a variety of fields that are concerned with the analysis of information transfer in complex biological, social, and artificial systems.Comment: 27 pages, 7 figures, submitted to PLOS ON

On the Impact of the Degree of Fluorination on the ORR Limiting Processes within Iron Based Catalysts: A Model Study on Symmetrical Films of Barium Ferrate

In this study, symmetrical films of BaFeO$_{2.67}$, BaFeO$_{2.33}$F$_{0.33}$ and BaFeO$_{2}$F were synthesized and the oxygen uptake and conduction was investigated by high temperature impedance spectroscopy under an oxygen atmosphere. The data were analyzed on the basis of an impedance model designed for highly porous mixed ionic electronic conducting (MIEC) electrodes. Variable temperature X-ray diffraction experiments were utilized to estimate the stability window of the oxyfluoride compounds, which yielded a degradation temperature for BaFeO$_{2.33}$F$_{0.33}$ of 590 °C and a decomposition temperature for BaFeO$_{2}$F of 710 °C. The impedance study revealed a significant change of the catalytic behavior in dependency of the fluorine content. BaFeO$_{2.67}$ revealed a bulk-diffusion limited process, while BaFeO$_{2.33}$F$_{0.33}$ appeared to exhibit a fast bulk diffusion and a utilization region δ larger than the electrode thickness L (8 μm). In contrast, BaFeO$_{2}$F showed very area specific resistances due to the lack of oxygen vacancies. The activation energy for the uptake and conduction process of oxygen was found to be 0.07/0.29 eV (temperature range-dependent), 0.33 eV and 0.67 eV for BaFeO$_{2.67}$, BaFeO$_{2.33}$F$_{0.33}$ and BaFeO$_{2}$F, respectively

Precision and Recall Reject Curves for Classification

For some classification scenarios, it is desirable to use only those classification instances that a trained model associates with a high certainty. To obtain such high-certainty instances, previous work has proposed accuracy-reject curves. Reject curves allow to evaluate and compare the performance of different certainty measures over a range of thresholds for accepting or rejecting classifications. However, the accuracy may not be the most suited evaluation metric for all applications, and instead precision or recall may be preferable. This is the case, for example, for data with imbalanced class distributions. We therefore propose reject curves that evaluate precision and recall, the recall-reject curve and the precision-reject curve. Using prototype-based classifiers from learning vector quantization, we first validate the proposed curves on artificial benchmark data against the accuracy reject curve as a baseline. We then show on imbalanced benchmarks and medical, real-world data that for these scenarios, the proposed precision- and recall-curves yield more accurate insights into classifier performance than accuracy reject curves.Comment: 11 pages, 3 figures. Updated figure label

Sentinel-1 Imaging Performance Verification with TerraSAR-X

This paper presents dedicated analyses of TerraSAR-X data with respect to the Sentinel-1 TOPS imaging mode. First, the analysis of Doppler centroid behaviour for high azimuth steering angles, as occurs in TOPS imaging, is investigated followed by the analysis and compensation of residual scalloping. Finally, the Flexible-Dynamic BAQ (FD-BAQ) raw data compression algorithm is investigated for the first time with real TerraSAR-X data and its performance is compared to state-of-the-art BAQ algorithms. The presented analyses demonstrate the improvements of the new TOPS imaging mode as well as the new FD-BAQ data compression algorithm for SAR image quality in general and in particular for Sentinel-1

Understanding Concept Identification as Consistent Data Clustering Across Multiple Feature Spaces

Identifying meaningful concepts in large data sets can provide valuable insights into engineering design problems. Concept identification aims at identifying non-overlapping groups of design instances that are similar in a joint space of all features, but which are also similar when considering only subsets of features. These subsets usually comprise features that characterize a design with respect to one specific context, for example, constructive design parameters, performance values, or operation modes. It is desirable to evaluate the quality of design concepts by considering several of these feature subsets in isolation. In particular, meaningful concepts should not only identify dense, well separated groups of data instances, but also provide non-overlapping groups of data that persist when considering pre-defined feature subsets separately. In this work, we propose to view concept identification as a special form of clustering algorithm with a broad range of potential applications beyond engineering design. To illustrate the differences between concept identification and classical clustering algorithms, we apply a recently proposed concept identification algorithm to two synthetic data sets and show the differences in identified solutions. In addition, we introduce the mutual information measure as a metric to evaluate whether solutions return consistent clusters across relevant subsets. To support the novel understanding of concept identification, we consider a simulated data set from a decision-making problem in the energy management domain and show that the identified clusters are more interpretable with respect to relevant feature subsets than clusters found by common clustering algorithms and are thus more suitable to support a decision maker.Comment: 10 pages, 6 figures, to be published in proceedings of 2022 IEEE International Conference on Data Mining Workshops (ICDMW

In-Orbit SAR Performance of TerraSAR-X

TerraSAR-X is the first German Radar satellite for scientific and commercial applications. The project is a public-private partnership between DLR and EADS Astrium GmbH. TerraSAR-X consists of a high resolution Synthetic Aperture Radar at X-Band. The radar antenna is based on active phased array technology that allows the control of many different instrument parameters and operational modes (Stripmap, ScanSAR and Spotlight) with various polarizations. Following the TerraSAR-X launch, scheduled for February 2007, it is planned a six month Commissioning Phase covering the characterization and verification of the SAR mission. Within this phase, the Overall SAR System Performance (OSSP) takes care of the correct working and interaction of all SAR system elements essential for obtaining an optimum SAR Performance. The paper covers the first in-orbit characterization and verification results of the SAR system performance for TerraSAR-X operational and experimental modes. This characterization is divided into four phases: Initial Characterization, Scene Characterization –both mostly based on basic and experimental products-, and Verification of TS-X Instrument Command Generation. The different optimization strategies and performance trade-offs are discussed and presented in the paper, including very first TerraSAR-X images. The result of the real SAR data analysis determines the final system baseline and thus the final image quality, e.g. Temperature compensation, Total Zero Doppler Steering, Up/down chirp toggling, transmitted bandwidth, timing interferences, etc. The first section of the paper introduces the activities carried out during the Commissioning Phase for the TerraSAR-X SAR system performance characterization/verification. In the second section, the strategies for the performance optimization and characterization are presented. Finally, the in-orbit SAR performance results are given in section three

Quantifying the predictability of visual scanpaths using active information storage

Entropy-based measures are an important tool for studying human gaze behavior under various conditions. In particular, gaze transition entropy (GTE) is a popular method to quantify the predictability of fixation transitions. However, GTE does not account for temporal dependencies beyond two consecutive fixations and may thus underestimate a scanpath's actual predictability. Instead, we propose to quantify scanpath predictability by estimating the active information storage (AIS), which can account for dependencies spanning multiple fixations. AIS is calculated as the mutual information between a processes' multivariate past state and its next value. It is thus able to measure how much information a sequence of past fixations provides about the next fixation, hence covering a longer temporal horizon. Applying the proposed approach, we were able to distinguish between induced observer states based on estimated AIS, providing first evidence that AIS may be used in the inference of user states to improve human-machine interaction.Comment: 19 pages, 3 figure

Scalloping Correction in TOPS Imaging Mode SAR Data

This paper presents an investigation on scalloping correction in the TOPS imaging mode for SAR systems with electronically steered phased array antennas. A theoretical simulation of the scalloping is performed and two correction methods are introduced. The simulation is based on a general cardinal sine (sinc) antenna model as well as on the TerraSAR-X antenna model. Real TerraSAR-X data acquired over rainforest are used for demonstration and verification of the scalloping simulation and correction. Furthermore a calibration approach taking into account the special TOPS imaging mode properties is introduced